Artificial Intelligence For Wildfires: Scaling a Solution To Save Lives, Infrastructure, and The Environment

Founded in 2016, Sintecsys is a young and growing Brazilian company with a passion for its customers, local communities and the climate. Its real‑time fire outbreak detection and management system protects forests and agricultural land across seven Brazilian states and four biomes, helping companies reduce their asset losses, shielding nearby residents from smoke‑related respiratory illnesses and limiting CO₂ emissions that contribute to global warming. When a fire begins, the very first signs—a thin plume of smoke rising behind a tree line, a faint shift in the colour of the sky or a subtle flicker in warm air—are easily missed. Wildfires can spread quickly and become increasingly difficult to control, so early detection gives firefighters a chance to act before the situation escalates. Across Brazil, with its vast natural and agricultural landscapes, traditional monitoring methods struggle with inconsistent visibility and the limits of human attention. The sooner smoke is spotted, the more land, infrastructure and lives can be saved.

Sintecsys understood that early detection is not a nice‑to‑have but a necessity. The company realised that scaling its camera network and empowering its staff to interpret countless images would require an intelligent system capable of filtering out fog, glare and other look‑alike phenomena without missing a real outbreak. To strengthen this core capability, the team sought to incorporate ai to detect wildfires into its real‑time monitoring platform. By automating the arduous task of sifting through live video feeds and highlighting only genuine signs of smoke, Sintecsys hoped to both expand its customer base and improve service for existing clients. The decision to augment human monitors with artificial intelligence set the stage for the collaborative development journey that followed.

Collaborative Innovation: Building an AI‑Driven Detection Model

When Sintecsys decided to enhance its detection capability, it looked beyond its in‑house resources. Sintecsys partnered with the global platform Omdena to launch an eight‑week challenge that assembled a diverse team of 47 collaborators from 22 countries. Working closely with Sintecsys’ internal team, these contributors combined expertise in computer vision, machine learning, environmental data and real‑world image interpretation, and they organized themselves around different modelling approaches. The challenge was scoped to focus initially on daytime images because sunsets, sunrises and night‑time footage introduce additional complexity that would be addressed later.

Collaborators divided into several task groups, each led by a volunteer manager and oriented toward a promising approach. Some groups investigated lightweight MobileNet architectures suitable for processing images quickly on limited hardware, others explored semantic segmentation to delineate smoke regions pixel by pixel, and yet another group leveraged Convolutional Neural Networks to learn discriminative features. A dedicated dataset team created two training corpora by labelling nearly 9,000 images, drawing frames from video footage and still photographs and ensuring a balance of smoke and non‑smoke scenes. The team used label‑smoothing techniques to distinguish real smoke from “smoke‑like” anomalies such as camera glare, fog, clouds and boiler emissions, and applied upsampling to enhance details in low‑resolution images. Building this dataset required careful coordination: about twenty collaborators spent hours labeling images, cross‑checking each other’s work and ensuring consistency across the corpus. Coordination relied on shared tools, open channels and mutual support rather than strict hierarchies, which fostered a sense of ownership among the volunteers.

Throughout the project the collaborators used a bottom‑up, Agile approach. Task managers emerged from within the group, daily discussions facilitated peer review and technical debates, and weekly calls with the Sintecsys team ensured that development stayed aligned with operational needs. Contributors brought skills across disciplines and connected deeply with the mission because it involved protecting land in their own region. This open structure allowed multiple techniques to be tested in parallel and helped the team meet tight deadlines without sacrificing quality. The result of this collaborative innovation was a robust AI model that could detect smoke in daytime images 95 % to 97 % of the time while keeping false positives between 10 % and 33 %. Put simply, the system captured nearly all real outbreaks while drastically reducing false alarms.

Impact on Profit and Purpose

Integrating the automated, high‑quality AI model into Sintecsys’ detection network made a tangible difference to both business outcomes and social impact. By automating the first pass of smoke identification, the upgraded system slashed average detection time. In fact, Sintecsys reports that its detection time decreased from an average of 40 minutes to under five minutes, and that the resulting drop in acres burned translates into 90 % fewer losses of crops and trees. Less smoke in the air means better respiratory health for farmers and local residents, and fewer uncontrolled fires means less carbon released into the atmosphere. The model’s low false‑positive rate also reduces unnecessary alarms and dispatches, saving customers, farmers and fire brigades time and money.

The operational advantages quickly translate into financial benefits. When false alarms are curtailed, firefighting units and farm personnel avoid costly and disruptive mobilizations. When real fires are caught early, agricultural companies preserve more of their harvests and avoid damage to expensive infrastructure. For communities living near monitored areas, reliable early warnings offer peace of mind: people can focus on their daily activities knowing that real threats will be flagged promptly and that spurious alerts are less likely. These intangible benefits—trust, confidence and reduced anxiety—are important outcomes of a system that is both accurate and fast.

Beyond immediate gains, the project positioned Sintecsys to grow sustainably. A prototype system has been launched with one client, and full deployment to all customers was planned for end‑2020 or early 2021. Because the model operates automatically, scaling does not require a linear expansion of staff: new camera towers can be added without overwhelming human operators. Sintecsys also plans to replicate the model in other countries, tailoring it to different ecosystems and regulatory frameworks while preserving the core technology. Together with Omdena, the company is exploring enhancements such as detecting fire in nighttime images, incorporating satellite imagery and predicting where fires are most likely to start. Such innovations will help the system cover more terrain, operate around the clock and move from reactive detection to proactive risk mitigation.

Osmar Bambini, Sintecsys’ Head of Innovation, captures the value of the partnership succinctly: “Speed, accuracy, and power sum up my perception of Omdena. For Sintecsys, from now on Omdena is our official artificial intelligence partner.” His endorsement underscores how a well‑executed collaboration can bring together technology and purpose: Omdena provided the expertise and community, while Sintecsys supplied data, domain knowledge and a clear mission. The resulting partnership not only met technical goals but also aligned with broader environmental and social objectives.

Key Takeaways

The Sintecsys–Omdena collaboration demonstrates how thoughtful innovation can address a pressing environmental challenge while supporting business growth. Their joint AI solution delivered:

• High detection accuracy:The model detects smoke in daytime images 95 % to 97 % of the time.
• Reduced false alarms:A false‑positive rate between 10 % and 33 % means fewer unnecessary investigations
• Faster response:Average detection time dropped from 40 minutes to under five.
• Greater environmental and social benefit:Early warnings help prevent 90 % of potential crop and tree loss while protecting respiratory health and reducing CO₂ emissions.
• A scalable model:The approach scales across regions and is being adapted for nighttime detection, satellite integration and risk prediction.

Conclusion: A Model for Global Action

The Sintecsys story illustrates how modern technology can be leveraged to tackle age‑old problems. By combining local knowledge with international expertise, the project transformed manual monitoring into an intelligent, scalable service. The lessons extend beyond the Brazilian biomes it serves: in any region where fires threaten livelihoods and ecosystems, a similar model of collaboration, open innovation and purposeful application of AI can drive progress. The challenge facing Sintecsys is global—climate change and human activity continue to increase the frequency and intensity of wildfires—yet the response showcased here proves that collective action can yield powerful tools. As more organizations adopt AI for environmental resilience, the hope is that detection systems will grow smarter and more anticipatory, enabling societies to prevent disasters rather than merely respond to them.

This article is written by Tim McQuillin.

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